Handle HF dynamic cache

@t-vi is already working on this.

Since Nov 11, 2024.

Dynamic cache currently is updated during tracing, this is not good.

```py
from transformers.models.llama import LlamaForCausalLM, LlamaConfig
from transformers import DynamicCache

model_id = "meta-llama/Llama-3.2-1B"

config_args = LlamaConfig.get_config_dict(model_id)[0]
config_args['num_hidden_layers'] = 1
with torch.device('cuda'):
model = LlamaForCausalLM(LlamaConfig(**config_args)).to(torch.bfloat16).requires_grad_(False).eval()

jm = thunder.jit(model)

j_past_key_values = DynamicCache()
args1 = dict(
cache_position=torch.tensor([0, 1, 2, 3, 4, 5], device='cuda:0'),
input_ids=torch.tensor([[128000, 791, 1401, 311, 2324, 374]], device='cuda:0'),
inputs_embeds=None,
attention_mask=torch.tensor([[1, 1, 1, 1, 1, 1]], device='cuda:0'),
use_cache=True, return_dict= True
)
res = jm(past_key_values=j_past_key_values, **args1)
```

gives

```py
> res = jm(past_key_values=j_past_key_values, **args1)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py:1736: in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py:1747: in _call_impl
return forward_call(*args, **kwargs)
thunder/core/module.py:80: in forward
res = self._forward_fn(*args, **kwargs)
thunder/__init__.py:736: in fn_
cache_entry, inps, pro_to_epi = get_computation_and_inputs(*args, **kwargs)
thunder/core/langctxs.py:136: in _fn
result = fn(*args, **kwargs)
thunder/__init__.py:232: in cache_info_wrapper
res = fn(*args, **kwargs)
thunder/__init__.py:633: in get_computation_and_inputs
inps, pro_to_epi = pro(*args, **kwargs)
thunder.prologue_0:190: in prologue
check_number_type_and_value(i192, 0)
thunder/executors/pythonex.py:103: in _check_number_type_and_value_impl
utils.check(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

cond = False, s = . at 0x7fc162c6bf60>, exception_type =

def check(cond: bool, s: Callable[[], str], exception_type: type[Exception] = RuntimeError) -> None:
"""Helper function for raising an error_type (default: RuntimeError) if a boolean condition fails.

s is a callable producing a string to avoid string construction if the error check is passed.
"""
if not cond:
> raise exception_type(s())
E RuntimeError: Expected 6 to be equal to and have the type of 0

thunder/core/baseutils.py:107: RuntimeError
```

This is useful for llama inference.